Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Gestalt Principles of Perception

Gestalt Principles of Perception

Gestalt principles provide a framework for understanding how humans perceive objects as unified wholes within their context. These principles are essential in explaining the cognitive processes that make sense of complex visual stimuli by organizing them into coherent groups. One fundamental principle is proximity, which posits that objects located close to each other are perceived as a collective group. For instance, when dots are positioned near one another, the visual system interprets them...

Methods of Medium Optimization

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

Response Surface Methodology

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

RNA-binding protein ZMAT3 protects against MASLD by regulating SEPT11 mRNA stability.

Cellular signalling·2026

Same author

MMP9 Genotype and Systemic T-Cell Subsets Correlate With Structural and Functional Outcomes in Neovascular Age-Related Macular Degeneration.

Investigative ophthalmology & visual science·2026

Same author

Early Optic Nerve Head Swelling Rate Is Associated with Worsening of Visual Outcomes in Nonarteritic Anterior Ischemic Optic Neuropathy.

Ophthalmology·2026

Same author

Capmatinib targeted therapy in MET-fusion driven radiation induced glioma.

Frontiers in oncology·2026

Same author

Hepatoma-Derived Growth Factor Coordinates STAT3 Pathway and Exosome-Mediated Intrahepatic Crosstalk to Control Hepatic Steatosis and MASLD.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same author

THAP7-AS1 orchestrates IGF2BP3-m<sup>6</sup>A-dependent CCN2 mRNA stabilization to promote lymphatic metastasis in bladder cancer.

American journal of cancer research·2026

Search research articles

Related Experiment Video

Updated: May 16, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Optimal multiple surface segmentation with shape and context priors.

Qi Song¹, Junjie Bai, Mona K Garvin

¹Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA. song@ge.com

IEEE Transactions on Medical Imaging

|November 30, 2012

Summary

This summary is machine-generated.

This study introduces a novel 3D graph-theoretic approach for medical image segmentation, improving accuracy by incorporating shape and context priors. The new method significantly reduces surface positioning errors in optical coherence tomography images.

More Related Videos

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Published on: December 15, 2023

Related Experiment Videos

Last Updated: May 16, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Published on: December 15, 2023

Area of Science:

Medical Imaging
Computer Vision
Computational Anatomy

Background:

Medical image segmentation is complex due to weak boundaries, object deformation, and inter-object influence.
Accurate segmentation is crucial for quantitative analysis and diagnosis.

Purpose of the Study:

To develop a novel 3D graph-theoretic framework for multi-object segmentation.
To enhance segmentation accuracy by integrating shape and context prior knowledge.

Main Methods:

Utilized an arc-based graph representation with pair-wise energy terms.
Incorporated shape-prior and context-prior terms to penalize deviations from expected models.
Computed the globally optimal solution using maximum flow in polynomial time.

Main Results:

Validated on intraretinal layer segmentation in optical coherence tomography (OCT) images.
Achieved statistically significant improvement in segmentation accuracy compared to previous methods.
Reduced mean unsigned surface positioning error from 6.30 ±1.58 μm to 5.14 ±0.99 μm.

Conclusions:

The proposed method effectively addresses challenges in multi-object medical image segmentation.
Integrating shape and context priors via a graph-theoretic framework enhances segmentation precision.
This approach offers a robust solution for accurate intraretinal layer segmentation in OCT imaging.